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AI magazine
| AI magazine |
| Pubbl/distr/stampa | La Canada, CA, : American Association for Artificial Intelligence, 1980- |
| Disciplina | 001.53/5/05 |
| Soggetto topico |
Artificial intelligence
Artificial intelligence - Computer programs System design Artificial intelligence - Technological innovations Artificial intelligence - Industrial applications Artificial intelligence - Educational applications Intelligence artificielle Kunstmatige intelligentie |
| Soggetto genere / forma | Periodicals. |
| ISSN | 2371-9621 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996214136603316 |
| La Canada, CA, : American Association for Artificial Intelligence, 1980- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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AI magazine
| AI magazine |
| Pubbl/distr/stampa | La Canada, CA, : American Association for Artificial Intelligence, 1980- |
| Disciplina | 001.53/5/05 |
| Soggetto topico |
Artificial intelligence
Artificial intelligence - Computer programs System design Artificial intelligence - Technological innovations Artificial intelligence - Industrial applications Artificial intelligence - Educational applications Intelligence artificielle Kunstmatige intelligentie |
| Soggetto genere / forma | Periodicals. |
| ISSN | 2371-9621 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910145204803321 |
| La Canada, CA, : American Association for Artificial Intelligence, 1980- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial intelligence for data center operations (AI Ops) / / Austin Todd [and twelve others]
| Artificial intelligence for data center operations (AI Ops) / / Austin Todd [and twelve others] |
| Autore | Austin Todd |
| Pubbl/distr/stampa | Golden, CO : , : National Renewable Energy Laboratory, , May 2021 |
| Descrizione fisica | 1 online resource (17 pages) : color illustrations |
| Collana | NREL/TP |
| Soggetto topico |
Artificial intelligence - Computer programs
Energy consumption - United States Energy consumption |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Artificial intelligence for data center operations |
| Record Nr. | UNINA-9910716754303321 |
Austin Todd
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| Golden, CO : , : National Renewable Energy Laboratory, , May 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Artificial intelligent techniques for wireless communication and networking / / edited by R. Kanthavel, [and three others]
| Artificial intelligent techniques for wireless communication and networking / / edited by R. Kanthavel, [and three others] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2022] |
| Descrizione fisica | 1 online resource (330 pages) |
| Disciplina | 621.382 |
| Soggetto topico |
Artificial intelligence - Computer programs
Wireless communication systems |
| ISBN |
1-119-82179-7
1-119-82180-0 1-119-82178-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Table of Contents -- Title Page -- Copyright -- Preface -- 1 Comprehensive and Self-Contained Introduction to Deep Reinforcement Learning -- 1.1 Introduction -- 1.2 Comprehensive Study -- 1.3 Deep Reinforcement Learning: Value-Based and Policy-Based Learning -- 1.4 Applications and Challenges of Applying Reinforcement Learning to Real-World -- 1.5 Conclusion -- References -- 2 Impact of AI in 5G Wireless Technologies and Communication Systems -- 2.1 Introduction -- 2.2 Integrated Services of AI in 5G and 5G in AI -- 2.3 Artificial Intelligence and 5G in the Industrial Space -- 2.4 Future Research and Challenges of Artificial Intelligence in Mobile Networks -- 2.5 Conclusion -- References -- 3 Artificial Intelligence Revolution in Logistics and Supply Chain Management -- 3.1 Introduction -- 3.2 Theory-AI in Logistics and Supply Chain Market -- 3.3 Factors to Propel Business Into the Future Harnessing Automation -- 3.4 Conclusion -- References -- 4 An Empirical Study of Crop Yield Prediction Using Reinforcement Learning -- 4.1 Introduction -- 4.2 An Overview of Reinforcement Learning in Agriculture -- 4.3 Reinforcement Learning Startups for Crop Prediction -- 4.4 Conclusion -- References -- 5 Cost Optimization for Inventory Management in Blockchain and Cloud -- 5.1 Introduction -- 5.2 Blockchain: The Future of Inventory Management -- 5.3 Cost Optimization for Blockchain Inventory Management in Cloud -- 5.4 Cost Reduction Strategies in Blockchain Inventory Management in Cloud -- 5.5 Conclusion -- References -- 6 Review of Deep Learning Architectures Used for Identification and Classification of Plant Leaf Diseases -- 6.1 Introduction -- 6.2 Literature Review -- 6.3 Proposed Idea -- 6.4 Reference Gap -- 6.5 Conclusion -- References -- 7 Generating Art and Music Using Deep Neural Networks -- 7.1 Introduction -- 7.2 Related Works.
7.3 System Architecture -- 7.4 System Development -- 7.5 Algorithm-LSTM -- 7.6 Result -- 7.7 Conclusions -- References -- 8 Deep Learning Era for Future 6G Wireless Communications-Theory, Applications, and Challenges -- 8.1 Introduction -- 8.2 Study of Wireless Technology -- 8.3 Deep Learning Enabled 6G Wireless Communication -- 8.4 Applications and Future Research Directions -- Conclusion -- References -- 9 Robust Cooperative Spectrum Sensing Techniques for a Practical Framework Employing Cognitive Radios in 5G Networks -- 9.1 Introduction -- 9.2 Spectrum Sensing in Cognitive Radio Networks -- 9.3 Collaborative Spectrum Sensing for Opportunistic Access in Fading Environments -- 9.4 Cooperative Sensing Among Cognitive Radios -- 9.5 Cluster-Based Cooperative Spectrum Sensing for Cognitive Radio Systems -- 9.6 Spectrum Agile Radios: Utilization and Sensing Architectures -- 9.7 Some Fundamental Limits on Cognitive Radio -- 9.8 Cooperative Strategies and Capacity Theorems for Relay Networks -- 9.9 Research Challenges in Cooperative Communication -- 9.10 Conclusion -- References -- 10 Natural Language Processing -- 10.1 Introduction -- 10.2 Conclusions -- References -- 11 Class Level Multi-Feature Semantic Similarity-Based Efficient Multimedia Big Data Retrieval -- 11.1 Introduction -- 11.2 Literature Review -- 11.3 Class Level Semantic Similarity-Based Retrieval -- 11.4 Results and Discussion -- Conclusion -- References -- 12 Supervised Learning Approaches for Underwater Scalar Sensory Data Modeling With Diurnal Changes -- 12.1 Introduction -- 12.2 Literature Survey -- 12.3 Proposed Work -- 12.4 Results -- 12.5 Conclusion and Future Work -- References -- 13 Multi-Layer UAV Ad Hoc Network Architecture, Protocol and Simulation -- 13.1 Introduction -- 13.2 Background -- 13.3 Issues and Gap Identified -- 13.4 Main Focus of the Chapter -- 13.5 Mobility. 13.6 Routing Protocol -- 13.7 High Altitude Platforms (HAPs) -- 13.8 Connectivity Graph Metrics -- 13.9 Aerial Vehicle Network Simulator (AVENs) -- 13.10 Conclusion -- References -- 14 Artificial Intelligence in Logistics and Supply Chain -- 14.1 Introduction to Logistics and Supply Chain -- 14.2 Recent Research Avenues in Supply Chain -- 14.3 Importance and Impact of AI -- 14.4 Research Gap of AI-Based Supply Chain -- References -- 15 Hereditary Factor-Based Multi-Featured Algorithm for Early Diabetes Detection Using Machine Learning -- 15.1 Introduction -- 15.2 Literature Review -- 15.3 Objectives of the Proposed System -- 15.4 Proposed System -- 15.5 HIVE and R as Evaluation Tools -- 15.6 Decision Trees -- 15.7 Results and Discussions -- 15.8 Conclusion -- References -- 16 Adaptive and Intelligent Opportunistic Routing Using Enhanced Feedback Mechanism -- 16.1 Introduction -- 16.2 Related Study -- 16.3 System Model -- 16.4 Experiments and Results -- 16.5 Conclusion -- References -- 17 Enabling Artificial Intelligence and Cyber Security in Smart Manufacturing -- 17.1 Introduction -- 17.2 New Development of Artificial Intelligence -- 17.3 Artificial Intelligence Facilitates the Development of Intelligent Manufacturing -- 17.4 Current Status and Problems of Green Manufacturing -- 17.5 Artificial Intelligence for Green Manufacturing -- 17.6 Detailed Description of Common Encryption Algorithms -- 17.6.1 Triple DES (3DES)-(Triple Data Encryption Standard) -- 17.7 Current and Future Works -- 17.8 Conclusion -- References -- 18 Deep Learning in 5G Networks -- 18.1 5G Networks -- 18.2 Artificial Intelligence and 5G Networks -- 18.3 Deep Learning in 5G Networks -- Conclusion -- References -- 19 EIDR Umpiring Security Models for Wireless Sensor Networks -- 19.1 Introduction -- 19.2 A Review of Various Routing Protocols -- 19.3 Scope of Chapter. 19.4 Conclusions and Future Work -- References -- 20 Artificial Intelligence in Wireless Communication -- 20.1 Introduction -- 20.2 Artificial Intelligence: A Grand Jewel Mine -- 20.3 Wireless Communication: An Overview -- 20.4 Wireless Revolution -- 20.5 The Present Times -- 20.6 Artificial Intelligence in Wireless Communication -- 20.6.1 How the Two Worlds Collided -- 20.6.2 Cognitive Radios -- 20.7 Artificial Neural Network -- 20.8 The Deployment of 5G -- 20.9 Looking Into the Features of 5G -- 20.10 AI and the Internet of Things (IoT) -- 20.11 Artificial Intelligence in Software-Defined Networks (SDN) -- 20.12 Artificial Intelligence in Network Function Virtualization -- 20.13 Conclusion -- References -- Index -- Also of Interest -- End User License Agreement. |
| Record Nr. | UNINA-9910830845703321 |
| Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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The definitive guide to conversational AI with Dialogflow and Google Cloud : build advanced enterprise chatbots, voice, and telephony agents on Google Cloud / / Lee Boonstra
| The definitive guide to conversational AI with Dialogflow and Google Cloud : build advanced enterprise chatbots, voice, and telephony agents on Google Cloud / / Lee Boonstra |
| Autore | Boonstra Lee |
| Pubbl/distr/stampa | [Place of publication not identified] : , : Apress, , [2021] |
| Descrizione fisica | 1 online resource (421 pages) |
| Disciplina | 006.3 |
| Soggetto topico | Artificial intelligence - Computer programs |
| ISBN | 1-4842-7014-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Table of Contents -- About the Author -- About the Technical Reviewers -- Acknowledgments -- Introduction -- Chapter 1: Introduction to Conversational AI -- The History of Text Chatbots -- Why Do Some Chatbots Fail? -- Machine Learning Simply Explained -- Natural Language Processing -- Chatbots and Artificial Intelligence -- Machine Learning and Google -- About Google Cloud -- Open Source -- About Dialogflow -- Dialogflow Essentials and Dialogflow CX -- How Dialogflow Essentials Works -- How the Industry Is Changing Its Complexity -- Where Dialogflow CX Fits In -- Dialogflow CX Explained -- Dialogflow Essentials vs. Dialogflow CX -- About Contact Center AI -- CCAI Architecture -- About Google Cloud Speech Technology -- Cloud Speech-to-Text API -- Cloud Text-to-Speech API -- WaveNet -- Custom Voice -- Other Google Conversational AI Products -- Google Assistant -- Actions on Google -- Actions Builder -- AdLingo -- Chatbase -- Duplex -- Meena & -- LaMDA -- Summary -- Further Reading -- Chapter 2: Getting Started with Dialogflow Essentials -- Dialogflow Essentials Editions -- Creating a Dialogflow Trial Agent -- Creating Dialogflow Agents for Enterprises -- Quotas -- User Roles and Monitoring -- Using VPC Service Controls -- Using Developer Features -- Configuring Your Dialogflow Project -- General -- Languages -- ML Settings -- Automatic Spell Correction -- Automatic Training -- Agent Validation -- Export and Import -- Environments -- Speech -- Improve Speech Recognition Quality -- Enable Enhanced Speech Models and Data Logging -- Enable Auto Speech Adaptation -- Text to Speech -- Enable Automatic Text to Speech -- Voice Configuration -- Share -- Advanced -- Configuring Your Dialogflow for Developers -- Summary -- Further Reading -- Chapter 3: Dialogflow Essentials Concepts -- Intents in Depth -- Setting Up Intents -- Entities in Depth.
Creating Custom Entities -- Advanced Custom Entities -- Creating Intents with Entities in Training Phrases -- Keeping Context -- Setting Up Follow-Up Intents -- Manually Setting Input and Output in "Normal" Intents -- Lifespan -- Keeping Context with the SDK -- Testing in the Simulator -- Summary -- Further Reading -- Chapter 4: Building Chatbots with Templates -- Creating Prebuilt Agents -- Enabling Small Talk Modules -- Creating an FAQ Knowledge Base -- Best Practices -- Convert Knowledge Base Questions to Intents -- Summary -- Further Reading -- Chapter 5: Bot Management -- Agent Validation -- Understanding Validation Results -- Validation via the SDK -- Improve the Dialogflow Machine Learning Model with Built-in Training -- Summary -- Further Reading -- Chapter 6: Deploying Your Chatbot to Web and Social Media Channels -- Integrating Your Agent with Google Chat -- Enabling Your Agent in the Google Chat -- Rich Messages Support -- More Text-Based/Open Source Integration Options -- Integrating Your Agent with a Web Demo -- Integrating Your Agent with a Dialogflow Messenger -- Changing the Look and Feel of the Chatbot Component -- Rich Messages Support -- Summary -- Further Reading -- Chapter 7: Building Voice Agents -- Building a Voice AI for a Virtual Assistant Like the Google Assistant -- Rich Messages -- Fulfillment and Webhooks -- Invoke Your Action on the Google Assistant with Explicit and Implicit Invocation -- Submitting an Action via Actions on Google -- Building an Action with the Actions SDK -- Using the Actions SDK Solution -- Deploying Your Action -- Building a Callbot with a Phone Gateway -- Response Messages for the Phone Gateway -- Building Bots for Contact Centers with Contact Center AI -- Enabling Contact Center AI -- Improving Speech to Text Quality -- Custom Entities Hints -- System Entities Hints -- Intent Hints. Overriding Speech Hints in Your Code -- Fine-Tuning the Text to Speech Output of Voice Bots with SSML -- UX Design for Voice Dialogues Matters! -- Text to Speech Voices -- Controlling the Intonation -- Summary -- Further Reading -- Chapter 8: Creating a Multilingual Chatbot -- Agent Languages -- Building a Multi-language Agent -- Exporting a Multi-language Dialogflow ES Agent -- Detecting Multi-language Intents via the SDK -- Working with the Translation Service -- Summary -- Further Reading -- Chapter 9: Orchestrate Multiple Sub-chatbots from One Chat Interface -- Creating a Mega-Agent -- Using the SDK -- How Billing Works -- Summary -- Further Reading -- Chapter 10: Creating Fulfillment Webhooks -- An Introduction to Fulfillment Webhooks -- Building a Fulfillment with the Built-in Editor -- Enable Fulfillment -- Using the dialogflow-fulfillment Package -- Diagnostic Info -- Firebase Logs -- Using Actions on Google for Building Dialogflow Fulfillment -- Build Your Fulfillment Webhook Manually -- Building Fulfillments Webhook -- Where to Run My Back-End Code? -- Cloud Functions -- App Engine (Flexible Environment) -- Cloud Run -- Kubernetes Engine -- Compute Engine -- Enable Webhooks -- Cloud Function Implementation -- Express Implementation (with Cloud Run) -- Google Cloud Logging -- Building Multilingual Fulfillment Webhook -- i18n Code Example -- Using Local Webhooks -- Ngrok -- Testing Your Fulfillment Without Dialogflow and ngrok -- Securing Webhooks -- Basic Authentication -- Authentication with Authentication Headers -- Mutual TLS Authentication -- Valid Secure SSL Certificate -- Root CA -- HTTPS Authentication Setup with Apache -- A Full Example for Setting Up Mutual TLS Authentication -- Create a Node.js VM on Compute Engine -- Attach a Domain Name to Your VM -- Set Up Your Node Application -- Set Up mTLS -- Summary -- Further Reading. Chapter 11: Creating a Custom Integration with the Dialogflow SDK -- Implementing a Custom Chatbot in Your Website Front End, Setup -- UI Implementation -- Back-End Implementation -- Welcome Message -- Creating Rich Responses in Your Chatbot Integration -- A Hyperlink Component, a Google Map, and an Image Component -- Implementation -- Using Markdown Syntax and Conditional Templates in Your Dialogflow Responses -- Branching the Conversation -- Building an Integration to Run a Dialogflow Agent in a Native Mobile Android or iOS App with Flutter -- Two Techniques for Integrating Dialogflow in a Flutter Application -- Integrating the Dialogflow SDK Directly into Your Flutter App -- A Flutter App That Communicates with a Back-End Dialogflow SDK App -- Summary -- Further Reading -- Chapter 12: Implementing a Dialogflow Voice Agent in Your Website or App Using the SDK -- Reasons for Not Picking Google Assistant -- Building a Client-Side Web Application That Streams Audio from a Browser Microphone to a Server -- Build the Front End -- Short Utterance vs. Streaming -- Record Single Utterances -- Record Audio Streams -- Building a Web Server That Receives a Browser Microphone Stream to Detect Intents -- Dialogflow vs. Text-to-Speech API vs. Speech-to-Text API -- Speech-to-Text API -- Text-to-Speech API -- Build the Back End -- API Calls to Dialogflow -- DetectIntent -- StreamingDetectIntent -- Retrieving Audio Results from Dialogflow and Playing It in Your Browser -- Client-Side Code to Play the Audio -- Summary -- Further Reading -- Chapter 13: Collecting and Monitoring Conversational Analytics -- Conversation-Related Metrics -- Customer Rating Metrics -- Chat Session and Funnel Metrics -- Bot Model Health Metrics -- Capturing Conversation-Related Metrics to Store in BigQuery -- BigQuery -- Capture Points -- Session Id -- Date/Timestamp -- Sentiment Score. Language and Keyword -- Platform -- Intent Detection -- Solutions -- Building a Platform for Capturing Conversation-Related Metrics and Redacting Sensitive Information -- Detecting User Sentiment -- Topic Mining -- Collecting Customer Rating Metrics -- Net Promoter Score (NPS) -- Customer Satisfaction (CSAT) -- Customer Effort Score (CES) -- Monitoring Chat Session and Funnel Metrics with Dialogflow, Chatbase, or Actions on Google -- Metrics to Monitor -- Total Usage -- Percentage of Users That Matches the Intent -- Completion Rate -- Drop-Off Rate/Drop-Off Place -- Channel-Specific Metrics to Monitor -- User Retention -- Endpoint Health -- Discovery -- Dialogflow Built-in Analytics -- Monitoring Metrics with Chatbase -- Analytics on Actions on Google -- Capturing Chatbot Model Health Metrics for Testing the Underlying NLU Model Quality -- True Positive-A Correctly Matched Intent -- True Negative-An Unsupported Request -- False Positive-A Misunderstood Request -- False Negative-A Missed Request -- True Positive Rate -- False Positive Rate -- ROC Curve -- Accuracy -- Precision -- F1 Score -- Confusion Matrix -- Summary -- Further Reading -- Appendix: An Introduction to Dialogflow CX -- How the Industry Is Changing Its Conversation Complexity -- Where Dialogflow CX Fits In -- Dialogflow CX Features -- New Concepts in Dialogflow CX -- Flows -- Pages -- State Handlers -- Contact Center Features -- Customer-Managed Encryption Keys (CMEKs) -- Where Both Products Differ -- Agents -- NLU -- Analytics -- Entities -- Intents -- Fulfillment and Webhooks -- APIs -- Error Handling -- When to Use Dialogflow CX vs. Dialogflow ES? -- Summary -- Further Reading -- Index. |
| Record Nr. | UNINA-9910488724603321 |
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Boonstra Lee
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| [Place of publication not identified] : , : Apress, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Demystifying Azure AI [[electronic resource] ] : Implementing the Right AI Features for Your Business / / by Kasam Shaikh
| Demystifying Azure AI [[electronic resource] ] : Implementing the Right AI Features for Your Business / / by Kasam Shaikh |
| Autore | Shaikh Kasam |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress : , : Imprint : Apress, , 2020 |
| Descrizione fisica | 1 online resource (196 pages) |
| Disciplina | 006.3 |
| Soggetto topico |
Microsoft Azure (Computing platform)
Artificial intelligence - Computer programs Business - Data processing Microsoft .NET Framework |
| ISBN | 1-4842-6219-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: Working with Azure Cognitive Search -- Chapter 2: AI and Backend Service Offerings -- Chapter 3: AI Services with Azure iPaaS -- Chapter 4: AI Services with Server-less Offerings -- Chapter 5: AI with Low Code. |
| Record Nr. | UNINA-9910416139003321 |
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Shaikh Kasam
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| Berkeley, CA : , : Apress : , : Imprint : Apress, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Exploring the Power of ChatGPT [[electronic resource] ] : Applications, Techniques, and Implications / / by Eric Sarrion
| Exploring the Power of ChatGPT [[electronic resource] ] : Applications, Techniques, and Implications / / by Eric Sarrion |
| Autore | Sarrion Eric |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress : , : Imprint : Apress, , 2023 |
| Descrizione fisica | 1 online resource (206 pages) |
| Disciplina | 006.3 |
| Soggetto topico |
Artificial intelligence - Computer programs
Artificial intelligence - Industrial applications |
| ISBN | 1-4842-9529-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 - What is ChatGPT? -- 2 - How Does ChatGPT Work? -- 3 - Applications of ChatGPT -- 4 - ChatGPT Training -- 5 - Using ChatGPT in Development Projects -- 6 - Best Practices for using ChatGPT -- 7 - Potential Biases and Risks of ChatGPT -- 8 - The Implications of ChatGPT for Employment and Society -- 9 - Regulations and Standards for using ChatGPT -- 10 - Future Developments of ChatGPT -- 11 - The Long-term Outlook for ChatGPT -- 12 - Using ChatGPT for Text Content Creation for Businesses -- 13 - Using ChatGPT for Text Translation -- 14 - Using ChatGPT to Learn a Language -- 15 - Using ChatGPT for Recruitment in Businesses -- 16 - Using ChatGPT for Code Generation in Computer Programs -- 17 - Using ChatGPT for artistic content creation -- 18 - Using ChatGPT for Innovation and Creativity -- 19 - Conclusion. |
| Record Nr. | UNINA-9910741136703321 |
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Sarrion Eric
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| Berkeley, CA : , : Apress : , : Imprint : Apress, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Foundations of decision-making agents [[electronic resource] ] : logic, probability and modality / / Subrata Das
| Foundations of decision-making agents [[electronic resource] ] : logic, probability and modality / / Subrata Das |
| Autore | Das Subrata Kumar |
| Pubbl/distr/stampa | Singapore ; ; Hackensack, NJ, : World Scientific |
| Descrizione fisica | 1 online resource (385 p.) |
| Disciplina | 006.33 |
| Soggetto topico |
Intelligent agents (Computer software)
Artificial intelligence - Computer programs |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-281-93816-5
9786611938161 981-277-984-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | ch. 1. Modeling agent epistemic states: an informal overview. 1.1. Models of agent epistemic states. 1.2. Propositional epistemic model. 1.3. Probabilistic epistemic model. 1.4. Possible world epistemic model. 1.5. Comparisons of models. 1.6. P3 model for decision-making agents -- ch. 2. Mathematical preliminaries. 2.1. Usage of symbols. 2.2. Sets, relations, and functions. 2.3. Graphs and trees. 2.4. Probability. 2.5. Algorithmic complexity -- ch. 3. Classical logics for the propositional epistemic model. 3.1. Propositional logic. 3.2. First-order logic. 3.3. Theorem proving procedure. 3.4. Resolution theorem proving. 3.5. Refutation procedure. 3.6. Complexity analysis -- ch. 4. Logic programming. 4.1. The concept. 4.2. Program clauses and goals. 4.3. Program semantics. 4.4. Definite programs. 4.5. Normal programs. 4.6. Prolog. 4.7. Prolog systems. 4.8. Complexity analysis -- ch. 5. Logical rules for making decisions. 5.1. Evolution of rules. 5.2. Bayesian probability theory for handling uncertainty. 5.3. Dempster-Shafer theory for handling uncertainty. 5.4. Measuring consensus. 5.5. Combining sources of varying confidence. 5.6. Advantages and disadvantages of rule-based systems -- ch. 6. Bayesian belief networks. 6.1. Bayesian belief networks. 6.2. Conditional independence in belief networks. 6.3. Evidence, belief, and likelihood. 6.4. Prior probabilities in networks without evidence. 6.5. Belief revision. 6.6. Evidence propagation in polytrees. 6.7. Evidence propagation in directed acyclic graphs. 6.8. Complexity of inference algorithms. 6.9. Acquisition of probabilities. 6.10. Advantages and disadvantages of belief networks. 6.11. Belief network tools -- ch. 7. Influence diagrams for making decisions. 7.1. Expected utility theory and decision trees. 7.2. Influence diagrams. 7.3. Inferencing in influence diagrams. 7.4. Compilation of influence diagrams. 7.5. Inferencing in strong junction tress -- ch. 8. Modal logics for the possible world epistemic model. 8.1. Historical development of modal logics. 8.2. Systems of modal logic. 8.3. Deductions in modal systems. 8.4. Modality. 8.5. Decidability and matrix method. 8.6. Relationships among modal systems. 8.7. Possible world semantics. 8.8. Soundness and completeness results. 8.9. Complexity and decidability of modal systems. 8.10. Modal first-order logics. 8.11. Resolution in modal first-order logics. 8.12. Modal epistemic logics. 8.13. Logic of agents beliefs (LAB) -- ch. 9. Symbolic argumentation for decision-making. 9.1. Toulmin's model of argumentation. 9.2. Domino decision-making model for P3. 9.3. Knowledge representation syntax of P3. 9.4. Formalization of P3 via LAB. 9.5. Aggregation via Dempster-Shafer theory. 9.6. Aggregation via Bayesian belief networks. |
| Record Nr. | UNINA-9910453551803321 |
|
Das Subrata Kumar
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||
| Singapore ; ; Hackensack, NJ, : World Scientific | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Foundations of decision-making agents [[electronic resource] ] : logic, probability and modality / / Subrata Das
| Foundations of decision-making agents [[electronic resource] ] : logic, probability and modality / / Subrata Das |
| Autore | Das Subrata Kumar |
| Pubbl/distr/stampa | Singapore ; ; Hackensack, NJ, : World Scientific |
| Descrizione fisica | 1 online resource (385 p.) |
| Disciplina | 006.33 |
| Soggetto topico |
Intelligent agents (Computer software)
Artificial intelligence - Computer programs |
| ISBN |
1-281-93816-5
9786611938161 981-277-984-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | ch. 1. Modeling agent epistemic states: an informal overview. 1.1. Models of agent epistemic states. 1.2. Propositional epistemic model. 1.3. Probabilistic epistemic model. 1.4. Possible world epistemic model. 1.5. Comparisons of models. 1.6. P3 model for decision-making agents -- ch. 2. Mathematical preliminaries. 2.1. Usage of symbols. 2.2. Sets, relations, and functions. 2.3. Graphs and trees. 2.4. Probability. 2.5. Algorithmic complexity -- ch. 3. Classical logics for the propositional epistemic model. 3.1. Propositional logic. 3.2. First-order logic. 3.3. Theorem proving procedure. 3.4. Resolution theorem proving. 3.5. Refutation procedure. 3.6. Complexity analysis -- ch. 4. Logic programming. 4.1. The concept. 4.2. Program clauses and goals. 4.3. Program semantics. 4.4. Definite programs. 4.5. Normal programs. 4.6. Prolog. 4.7. Prolog systems. 4.8. Complexity analysis -- ch. 5. Logical rules for making decisions. 5.1. Evolution of rules. 5.2. Bayesian probability theory for handling uncertainty. 5.3. Dempster-Shafer theory for handling uncertainty. 5.4. Measuring consensus. 5.5. Combining sources of varying confidence. 5.6. Advantages and disadvantages of rule-based systems -- ch. 6. Bayesian belief networks. 6.1. Bayesian belief networks. 6.2. Conditional independence in belief networks. 6.3. Evidence, belief, and likelihood. 6.4. Prior probabilities in networks without evidence. 6.5. Belief revision. 6.6. Evidence propagation in polytrees. 6.7. Evidence propagation in directed acyclic graphs. 6.8. Complexity of inference algorithms. 6.9. Acquisition of probabilities. 6.10. Advantages and disadvantages of belief networks. 6.11. Belief network tools -- ch. 7. Influence diagrams for making decisions. 7.1. Expected utility theory and decision trees. 7.2. Influence diagrams. 7.3. Inferencing in influence diagrams. 7.4. Compilation of influence diagrams. 7.5. Inferencing in strong junction tress -- ch. 8. Modal logics for the possible world epistemic model. 8.1. Historical development of modal logics. 8.2. Systems of modal logic. 8.3. Deductions in modal systems. 8.4. Modality. 8.5. Decidability and matrix method. 8.6. Relationships among modal systems. 8.7. Possible world semantics. 8.8. Soundness and completeness results. 8.9. Complexity and decidability of modal systems. 8.10. Modal first-order logics. 8.11. Resolution in modal first-order logics. 8.12. Modal epistemic logics. 8.13. Logic of agents beliefs (LAB) -- ch. 9. Symbolic argumentation for decision-making. 9.1. Toulmin's model of argumentation. 9.2. Domino decision-making model for P3. 9.3. Knowledge representation syntax of P3. 9.4. Formalization of P3 via LAB. 9.5. Aggregation via Dempster-Shafer theory. 9.6. Aggregation via Bayesian belief networks. |
| Record Nr. | UNINA-9910782274603321 |
|
Das Subrata Kumar
|
||
| Singapore ; ; Hackensack, NJ, : World Scientific | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Foundations of decision-making agents [[electronic resource] ] : logic, probability and modality / / Subrata Das
| Foundations of decision-making agents [[electronic resource] ] : logic, probability and modality / / Subrata Das |
| Autore | Das Subrata Kumar |
| Pubbl/distr/stampa | Singapore ; ; Hackensack, NJ, : World Scientific |
| Descrizione fisica | 1 online resource (385 p.) |
| Disciplina | 006.33 |
| Soggetto topico |
Intelligent agents (Computer software)
Artificial intelligence - Computer programs |
| ISBN |
1-281-93816-5
9786611938161 981-277-984-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | ch. 1. Modeling agent epistemic states: an informal overview. 1.1. Models of agent epistemic states. 1.2. Propositional epistemic model. 1.3. Probabilistic epistemic model. 1.4. Possible world epistemic model. 1.5. Comparisons of models. 1.6. P3 model for decision-making agents -- ch. 2. Mathematical preliminaries. 2.1. Usage of symbols. 2.2. Sets, relations, and functions. 2.3. Graphs and trees. 2.4. Probability. 2.5. Algorithmic complexity -- ch. 3. Classical logics for the propositional epistemic model. 3.1. Propositional logic. 3.2. First-order logic. 3.3. Theorem proving procedure. 3.4. Resolution theorem proving. 3.5. Refutation procedure. 3.6. Complexity analysis -- ch. 4. Logic programming. 4.1. The concept. 4.2. Program clauses and goals. 4.3. Program semantics. 4.4. Definite programs. 4.5. Normal programs. 4.6. Prolog. 4.7. Prolog systems. 4.8. Complexity analysis -- ch. 5. Logical rules for making decisions. 5.1. Evolution of rules. 5.2. Bayesian probability theory for handling uncertainty. 5.3. Dempster-Shafer theory for handling uncertainty. 5.4. Measuring consensus. 5.5. Combining sources of varying confidence. 5.6. Advantages and disadvantages of rule-based systems -- ch. 6. Bayesian belief networks. 6.1. Bayesian belief networks. 6.2. Conditional independence in belief networks. 6.3. Evidence, belief, and likelihood. 6.4. Prior probabilities in networks without evidence. 6.5. Belief revision. 6.6. Evidence propagation in polytrees. 6.7. Evidence propagation in directed acyclic graphs. 6.8. Complexity of inference algorithms. 6.9. Acquisition of probabilities. 6.10. Advantages and disadvantages of belief networks. 6.11. Belief network tools -- ch. 7. Influence diagrams for making decisions. 7.1. Expected utility theory and decision trees. 7.2. Influence diagrams. 7.3. Inferencing in influence diagrams. 7.4. Compilation of influence diagrams. 7.5. Inferencing in strong junction tress -- ch. 8. Modal logics for the possible world epistemic model. 8.1. Historical development of modal logics. 8.2. Systems of modal logic. 8.3. Deductions in modal systems. 8.4. Modality. 8.5. Decidability and matrix method. 8.6. Relationships among modal systems. 8.7. Possible world semantics. 8.8. Soundness and completeness results. 8.9. Complexity and decidability of modal systems. 8.10. Modal first-order logics. 8.11. Resolution in modal first-order logics. 8.12. Modal epistemic logics. 8.13. Logic of agents beliefs (LAB) -- ch. 9. Symbolic argumentation for decision-making. 9.1. Toulmin's model of argumentation. 9.2. Domino decision-making model for P3. 9.3. Knowledge representation syntax of P3. 9.4. Formalization of P3 via LAB. 9.5. Aggregation via Dempster-Shafer theory. 9.6. Aggregation via Bayesian belief networks. |
| Record Nr. | UNINA-9910825819003321 |
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Das Subrata Kumar
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| Singapore ; ; Hackensack, NJ, : World Scientific | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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